Another adaptation technique presented in this paper is the model agnostic meta-learning (MAML) [1]} based adaptation. MAML focuses on learning good initialization parameters for a model trained on the source domain so that it quickly adapts to the target domain with little training data.
In this work, an audio segment of 5 seconds is considered an episode. Different audios differ in singers, genres, background instruments, etc. We aim at creating a model that generalizes well to any audio and improves further with little training data.
